Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	18,831
2	New Jersey	17,355
3	Massachusetts	13,445
4	Rhode Island	13,276
5	District of Columbia	11,491
6	Connecticut	11,350
7	Delaware	9,046
8	Illinois	8,723
9	Louisiana	8,022
10	Maryland	7,760
11	Nebraska	6,272
12	Pennsylvania	5,597
13	Iowa	5,525
14	Michigan	5,468
15	South Dakota	5,157
16	Indiana	4,775
17	Mississippi	4,452
18	Virginia	4,246
19	Colorado	4,191
20	Georgia	3,860
21	Minnesota	3,649
22	New Mexico	3,311
23	North Dakota	3,172
24	Kansas	3,135
25	New Hampshire	3,051
26	Alabama	2,952
27	Tennessee	2,945
28	Ohio	2,729
29	Washington	2,722
30	Wisconsin	2,638
31	Utah	2,617
32	Nevada	2,560
33	California	2,397
34	Florida	2,367
35	Arizona	2,244
36	North Carolina	2,214
37	Kentucky	1,979
38	Missouri	1,973
39	Arkansas	1,962
40	South Carolina	1,960
41	Texas	1,933
42	Vermont	1,532
43	Maine	1,528
44	Oklahoma	1,525
45	Idaho	1,482
46	Wyoming	1,404
47	Puerto Rico	998
48	West Virginia	988
49	Oregon	931
50	Alaska	559
51	Montana	448
52	Hawaii	447

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Illinois	198
2	Maryland	180
3	Rhode Island	155
4	District of Columbia	152
5	South Dakota	145
6	Delaware	144
7	Minnesota	140
8	Iowa	133
9	Massachusetts	125
10	Nebraska	122
11	Connecticut	117
12	Mississippi	115
13	New Jersey	100
14	New York	86
15	Wisconsin	84
16	North Dakota	82
17	Virginia	82
18	New Mexico	74
19	Indiana	73
20	North Carolina	73
21	Alabama	72
22	Georgia	63
23	Pennsylvania	62
24	Colorado	56
25	Tennessee	55
26	Utah	53
27	California	52
28	Nevada	52
29	New Hampshire	52
30	Arkansas	51
31	Ohio	49
32	Louisiana	47
33	Arizona	46
34	South Carolina	46
35	Maine	44
36	Kansas	41
37	Michigan	38
38	Florida	34
39	West Virginia	31
40	Oklahoma	30
41	Washington	30
42	Texas	29
43	Puerto Rico	28
44	Kentucky	27
45	Missouri	24
46	Idaho	21
47	Oregon	8
48	Wyoming	6
49	Alaska	3
50	Vermont	3
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,493
2	New Jersey	1,253
3	Connecticut	1,035
4	Massachusetts	924
5	District of Columbia	612
6	Louisiana	578
7	Rhode Island	573
8	Michigan	523
9	Pennsylvania	401
10	Illinois	385
11	Maryland	376
12	Delaware	334
13	Indiana	293
14	Colorado	231
15	Mississippi	210
16	Georgia	169
17	Ohio	168
18	Minnesota	155
19	New Hampshire	153
20	New Mexico	151
21	Iowa	144
22	Washington	140
23	Virginia	137
24	Nevada	127
25	Alabama	112
26	Missouri	112
27	Arizona	109
28	Florida	104
29	California	95
30	Kentucky	89
31	Wisconsin	87
32	Vermont	86
33	South Carolina	84
34	Nebraska	79
35	Oklahoma	78
36	North Carolina	73
37	North Dakota	69
38	Kansas	68
39	Maine	58
40	South Dakota	56
41	Texas	52
42	Tennessee	49
43	Idaho	44
44	West Virginia	40
45	Puerto Rico	39
46	Arkansas	38
47	Oregon	35
48	Utah	30
49	Wyoming	20
50	Montana	14
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	16
2	Connecticut	10
3	Massachusetts	10
4	New Jersey	10
5	District of Columbia	9
6	Illinois	6
7	Maryland	6
8	New York	6
9	Pennsylvania	6
10	Mississippi	5
11	Iowa	4
12	Louisiana	4
13	Delaware	3
14	Indiana	3
15	Michigan	3
16	Minnesota	3
17	New Mexico	3
18	Ohio	3
19	New Hampshire	2
20	Virginia	2
21	Alabama	1
22	Arizona	1
23	California	1
24	Colorado	1
25	Florida	1
26	Georgia	1
27	Maine	1
28	Missouri	1
29	Nevada	1
30	North Carolina	1
31	South Carolina	1
32	South Dakota	1
33	Tennessee	1
34	Wisconsin	1
35	Alaska	0
36	Arkansas	0
37	Hawaii	0
38	Idaho	0
39	Kansas	0
40	Kentucky	0
41	Montana	0
42	Nebraska	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,449	1	99
Dakota	Nebraska	79,696	2	99
Lincoln	Arkansas	74,785	3	99
Nobles	Minnesota	67,363	4	99
Lake	Tennessee	58,295	5	99
Davidson	Tennessee	6,816	264	91
Richland	South Carolina	3,274	664	78
Pierce	Washington	2,070	983	68
Orange	California	1,680	1156	63
York	South Carolina	1,210	1460	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	2,846	2	99
Randolph	Georgia	2,803	3	99
Hancock	Georgia	2,483	4	99
Essex	New Jersey	1,996	5	99
Richland	South Carolina	152	559	82
Davidson	Tennessee	82	831	73
Pierce	Washington	82	834	73
Orange	California	41	1196	61
York	South Carolina	21	1465	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.